Home About us Contact
|
Home About us Contact | ||
|
|
||
Wavelet Coefficients (wavelet + coefficient)
Selected AbstractsEstimate of input energy for elasto-plastic SDOF systems during earthquakes based on discrete wavelet coefficientsEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 15 2005Jun Iyama Abstract The response of an elasto-plastic single degree of freedom (SDOF) system to ground motion is estimated based on wavelet coefficients calculated by discrete wavelet transform. Wavelet coefficients represent both the time and frequency characteristics of input ground motion, and thus can be considered to be directly related to the dynamic response of a non-linear system. This relationship between the energy input into an elastic SDOF system and wavelet coefficients is derived based on the assumption that wavelets deliver energy to the structure instantaneously and the quantity of energy is constant regardless of yielding. These assumptions are shown to be valid when the natural period of the system is in the predominant period range of the wavelet, the most common scenario for real structures, through dynamic response analysis of a single wavelet. The wavelet-based estimation of elastic and plastic energy transferred by earthquake ground motion is thus shown to be in good agreement with the dynamic response analysis when the natural period is in the predominant range of the input. Copyright © 2005 John Wiley & Sons, Ltd. [source] Wavelet-based functional mixed modelsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2006Jeffrey S. Morris Summary., Increasingly, scientific studies yield functional data, in which the ideal units of observation are curves and the observed data consist of sets of curves that are sampled on a fine grid. We present new methodology that generalizes the linear mixed model to the functional mixed model framework, with model fitting done by using a Bayesian wavelet-based approach. This method is flexible, allowing functions of arbitrary form and the full range of fixed effects structures and between-curve covariance structures that are available in the mixed model framework. It yields nonparametric estimates of the fixed and random-effects functions as well as the various between-curve and within-curve covariance matrices. The functional fixed effects are adaptively regularized as a result of the non-linear shrinkage prior that is imposed on the fixed effects' wavelet coefficients, and the random-effect functions experience a form of adaptive regularization because of the separately estimated variance components for each wavelet coefficient. Because we have posterior samples for all model quantities, we can perform pointwise or joint Bayesian inference or prediction on the quantities of the model. The adaptiveness of the method makes it especially appropriate for modelling irregular functional data that are characterized by numerous local features like peaks. [source] Exploiting statistical properties of wavelet coefficient for face detection and recognitionPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2007Naseer Al-Jawad Wavelet transforms (WT) are widely accepted as an essential tool for image processing and analysis. Image and video compression, image watermarking, content-base image retrieval, face recognition, texture analysis, and image feature extraction are all but few examples. It provides an alternative tool for short time analysis of quasi-stationary signals, such as speech and image signals, in contrast to the traditional short-time Fourier transform. The Discrete Wavelet Transform (DWT) is a special case of the WT, which provides a compact representation of a signal in the time and frequency domain. In particular, wavelet transforms are capable of representing smooth patterns as well anomalies (e.g. edges and sharp corners) in images. We are focusing here on using wavelet transforms statistical properties for facial feature detection, which allows us to extract the image facial feature/edges easily. Wavelet sub-bands segmentation method been developed and used to clean up the non-significant wavelet coefficients in wavelet sub-band (k) based on the (k-1) sub-band. Moreover, erosion which is considered as one of the fundamental operation in morphological image processing, been used to reduce the unwanted edges in certain directions. For face detection, face template profiles been built for both the face and the eyes for different wavelet sub-band levels to achieve better computational performance, these profiles used to match the extracted profiles from the wavelet domain of the input image using the Dynamic Time Warping technique DTW. The DTW smallest distance allows identifying the face and the eyes location. The performance of face features distances and ratio has been also tested for face verification purposes. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Estimate of input energy for elasto-plastic SDOF systems during earthquakes based on discrete wavelet coefficientsEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 15 2005Jun Iyama Abstract The response of an elasto-plastic single degree of freedom (SDOF) system to ground motion is estimated based on wavelet coefficients calculated by discrete wavelet transform. Wavelet coefficients represent both the time and frequency characteristics of input ground motion, and thus can be considered to be directly related to the dynamic response of a non-linear system. This relationship between the energy input into an elastic SDOF system and wavelet coefficients is derived based on the assumption that wavelets deliver energy to the structure instantaneously and the quantity of energy is constant regardless of yielding. These assumptions are shown to be valid when the natural period of the system is in the predominant period range of the wavelet, the most common scenario for real structures, through dynamic response analysis of a single wavelet. The wavelet-based estimation of elastic and plastic energy transferred by earthquake ground motion is thus shown to be in good agreement with the dynamic response analysis when the natural period is in the predominant range of the input. Copyright © 2005 John Wiley & Sons, Ltd. [source] Noise removal for medical X-ray images in wavelet domainELECTRICAL ENGINEERING IN JAPAN, Issue 3 2008Ling Wang Abstract Many important problems in engineering and science are well-modeled by Poisson noise, and the noise of medical X-ray images is Poisson noise. In this paper, we propose a method for noise removal for degraded medical X-ray images using improved preprocessing and an improved BayesShrink (IBS) method in the wavelet domain. First, we preprocess the medical X-ray image. Second, we apply the Daubechies (db) wavelet transform to medical X-ray images to acquire scaling and wavelet coefficients. Third, we apply the proposed IBS method to process wavelet coefficients. Finally, we compute the inverse wavelet transform for the threshold coefficients. Experimental results show that the proposed method always outperforms traditional methods. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 37, 46, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20486 [source] Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signalsEXPERT SYSTEMS, Issue 2 2009Elif Derya Übeyli Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%). [source] Detection of electrocardiogram beats using a fuzzy similarity indexEXPERT SYSTEMS, Issue 2 2007Elif Derya Übeyli Abstract: A new approach based on the computation of a fuzzy similarity index (FSI) is presented for the detection of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analysed. The ECG signals were decomposed into time,frequency representations using the discrete wavelet transform and wavelet coefficients were calculated to represent the signals. The aim of the study is detection of ECG beats by the combination of wavelet coefficients and the FSI. Toward achieving this aim, fuzzy sets were obtained from the feature sets (wavelet coefficients) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the ECG signals. Thus, the FSI could discriminate the normal beat and the other three types of beats (congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat). [source] Combined neural network model to compute wavelet coefficientsEXPERT SYSTEMS, Issue 3 2006nan Güler Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients. [source] Bayesian estimation of evoked and induced responsesHUMAN BRAIN MAPPING, Issue 9 2006Karl Friston We describe an extension of our empirical Bayes approach to magnetoencephalography/electroencephalography (MEG/EEG) source reconstruction that covers both evoked and induced responses. The estimation scheme is based on classical covariance component estimation using restricted maximum likelihood (ReML). We have focused previously on the estimation of spatial covariance components under simple assumptions about the temporal correlations. Here we extend the scheme, using temporal basis functions to place constraints on the temporal form of the responses. We show how the same scheme can estimate evoked responses that are phase-locked to the stimulus and induced responses that are not. For a single trial the model is exactly the same. In the context of multiple trials, however, the inherent distinction between evoked and induced responses calls for different treatments of the underlying hierarchical multitrial model. We derive the respective models and show how they can be estimated efficiently using ReML. This enables the Bayesian estimation of evoked and induced changes in power or, more generally, the energy of wavelet coefficients. Hum Brain Mapp, 2006. © 2006 Wiley-Liss, Inc. [source] Real-time signal processing for high-density microelectrode array systemsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 11 2009K. Imfeld Abstract The microelectrode array (MEA) technology is continuously progressing towards higher integration of an increasing number of electrodes. The ensuing data streams that can be of several hundreds or thousands of Megabits/s require the implementation of new signal processing and data handling methodologies to substitute the currently used off-line analysis methods. Here, we present one approach based on the hardware implementation of a wavelet-based solution for real-time processing of extracellular neuronal signals acquired on high-density MEAs. We demonstrate that simple mathematical operations on the discrete wavelet transform (DWT) coefficients can be used for efficient neuronal spike detection and sorting. As the DWT is particularly well suited for implementation on dedicated hardware, we elaborated a wavelet processor on a field programmable gate array (FPGA) in order to compute the wavelet coefficients on 256 channels in real-time. By providing sufficient hardware resources, this solution can be easily scaled up for processing more electrode channels. Copyright © 2008 John Wiley & Sons, Ltd. [source] Image coding based on wavelet feature vectorINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 2 2005Shinfeng D. Lin Abstract In this article, an efficient image coding scheme that takes advantages of feature vector in wavelet domain is proposed. First, a multi-stage discrete wavelet transform is applied on the image. Then, the wavelet feature vectors are extracted from the wavelet-decomposed subimages by collecting the corresponding wavelet coefficients. And finally, the image is coded into bit-stream by applying vector quantization (VQ) on the extracted wavelet feature vectors. In the encoder, the wavelet feature vectors are encoded with a codebook where the dimension of codeword is less than that of wavelet feature vector. By this way, the coding system can greatly improve its efficiency. However, to fully reconstruct the image, the received indexes in the decoder are decoded with a codebook where the dimension of codeword is the same as that of wavelet feature vector. Therefore, the quality of reconstructed images can be preserved well. The proposed scheme achieves good compression efficiency by the following three methods. (1) Using the correlation among wavelet coefficients. (2) Placing different emphasis on wavelet coefficients at different decomposing levels. (3) Preserving the most important information of the image by coding the lowest-pass subimage individually. In our experiments, simulation results show that the proposed scheme outperforms the recent VQ-based image coding schemes and wavelet-based image coding techniques, respectively. Moreover, the proposed scheme is also suitable for very low bit rate image coding. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 123,130, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20045 [source] Multivariate analysis of congruent images (MACI)JOURNAL OF CHEMOMETRICS, Issue 5-7 2005Lennart Eriksson Abstract The multivariate analysis of congruent images (MACI) is discussed. Here, each image represents one observation and the data set contains a set of congruent images. With ,congruent images' we mean a set of images, properly pre-processed, oriented and aligned, so that each data element (,feature', pixel) corresponds to the same element across all images. An example may be a set of frames from a fixed video camera looking at a stable process. The purpose of a MACI is to find and express patterns over a set of images for the purpose of classification or quantitative regression-like relationships. This is in contrast to standard image analysis, which is usually concerned with a single image and the identification of parts of the image, for example tumour tissue versus normal. We also extend MACI to the case with a set of images that initially are not fully congruent, but are made so by the use of wavelet analysis and the distributions of the wavelet coefficients. Thus, the resulting description forms a set of congruent vectors amenable to multivariate data analysis. The MACI approach will be illustrated by four data sets, three easy-to-understand tutorial image data sets and one industrial image data set relating to quality control of steel rolls. Copyright © 2006 John Wiley & Sons, Ltd. [source] Wavelet-based functional mixed modelsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2006Jeffrey S. Morris Summary., Increasingly, scientific studies yield functional data, in which the ideal units of observation are curves and the observed data consist of sets of curves that are sampled on a fine grid. We present new methodology that generalizes the linear mixed model to the functional mixed model framework, with model fitting done by using a Bayesian wavelet-based approach. This method is flexible, allowing functions of arbitrary form and the full range of fixed effects structures and between-curve covariance structures that are available in the mixed model framework. It yields nonparametric estimates of the fixed and random-effects functions as well as the various between-curve and within-curve covariance matrices. The functional fixed effects are adaptively regularized as a result of the non-linear shrinkage prior that is imposed on the fixed effects' wavelet coefficients, and the random-effect functions experience a form of adaptive regularization because of the separately estimated variance components for each wavelet coefficient. Because we have posterior samples for all model quantities, we can perform pointwise or joint Bayesian inference or prediction on the quantities of the model. The adaptiveness of the method makes it especially appropriate for modelling irregular functional data that are characterized by numerous local features like peaks. [source] An adaptive empirical Bayesian thresholding procedure for analysing microarray experiments with replicationJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2007Rebecca E. Walls Summary., A typical microarray experiment attempts to ascertain which genes display differential expression in different samples. We model the data by using a two-component mixture model and develop an empirical Bayesian thresholding procedure, which was originally introduced for thresholding wavelet coefficients, as an alternative to the existing methods for determining differential expression across thousands of genes. The method is built on sound theoretical properties and has easy computer implementation in the R statistical package. Furthermore, we consider improvements to the standard empirical Bayesian procedure when replication is present, to increase the robustness and reliability of the method. We provide an introduction to microarrays for those who are unfamilar with the field and the proposed procedure is demonstrated with applications to two-channel complementary DNA microarray experiments. [source] Detecting and creating oscillations using multifractal methodsMATHEMATISCHE NACHRICHTEN, Issue 11 2006Stéphane Seuret Abstract By comparing the Hausdorff multifractal spectrum with the large deviations spectrum of a given continuous function f, we find sufficient conditions ensuring that f possesses oscillating singularities. Using a similar approach, we study the nonlinear wavelet threshold operator which associates with any function f = ,j ,kdj,k,j,k , L2(,) the function series ft whose wavelet coefficients are dtj,k = dj,k1, for some fixed real number , > 0. This operator creates a context propitious to have oscillating singularities. As a consequence, we prove that the series ft may have a multifractal spectrum with a support larger than the one of f . We exhibit an example of function f , L2(,) such that the associated thresholded function series ft effectively possesses oscillating singularities which were not present in the initial function f . This series ft is a typical example of function with homogeneous non-concave multifractal spectrum and which does not satisfy the classical multifractal formalisms. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Do wavelets really detect non-Gaussianity in the 4-year COBE data?MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 4 2000P. Mukherjee We investigate the detection of non-Gaussianity in the 4-year COBE data reported by Pando, Valls-Gabaud & Fang, using a technique based on the discrete wavelet transform. Their analysis was performed on the two DMR faces centred on the North and South Galactic poles, respectively, using the Daubechies 4 wavelet basis. We show that these results depend critically on the orientation of the data, and so should be treated with caution. For two distinct orientations of the data, we calculate estimates of the skewness, kurtosis and scale,scale correlation of the corresponding wavelet coefficients in all of the available scale domains of the transform. We obtain several detections of non-Gaussianity in the DMR-DSMB map at greater than the 99 per cent confidence level, but most of these occur on pixel,pixel scales and are therefore not cosmological in origin. Indeed, after removing all multipoles beyond ,=40 from the COBE maps, only one robust detection remains. Moreover, using Monte Carlo simulations, we find that the probability of obtaining such a detection by chance is 0.59. We repeat the analysis for the 53+90 GHz coadded COBE map. In this case, after removing ,>40 multipoles, two non-Gaussian detections at the 99 per cent level remain. Nevertheless, again using Monte Carlo simulations, we find that the probability of obtaining two such detections by chance is 0.28. Thus, we conclude the wavelet technique does not yield strong evidence for non-Gaussianity of cosmological origin in the 4-year COBE data. [source] Exploiting statistical properties of wavelet coefficient for face detection and recognitionPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2007Naseer Al-Jawad Wavelet transforms (WT) are widely accepted as an essential tool for image processing and analysis. Image and video compression, image watermarking, content-base image retrieval, face recognition, texture analysis, and image feature extraction are all but few examples. It provides an alternative tool for short time analysis of quasi-stationary signals, such as speech and image signals, in contrast to the traditional short-time Fourier transform. The Discrete Wavelet Transform (DWT) is a special case of the WT, which provides a compact representation of a signal in the time and frequency domain. In particular, wavelet transforms are capable of representing smooth patterns as well anomalies (e.g. edges and sharp corners) in images. We are focusing here on using wavelet transforms statistical properties for facial feature detection, which allows us to extract the image facial feature/edges easily. Wavelet sub-bands segmentation method been developed and used to clean up the non-significant wavelet coefficients in wavelet sub-band (k) based on the (k-1) sub-band. Moreover, erosion which is considered as one of the fundamental operation in morphological image processing, been used to reduce the unwanted edges in certain directions. For face detection, face template profiles been built for both the face and the eyes for different wavelet sub-band levels to achieve better computational performance, these profiles used to match the extracted profiles from the wavelet domain of the input image using the Dynamic Time Warping technique DTW. The DTW smallest distance allows identifying the face and the eyes location. The performance of face features distances and ratio has been also tested for face verification purposes. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Low-resolution phase extension using wavelet analysisACTA CRYSTALLOGRAPHICA SECTION D, Issue 10 2000Peter Main A method to extend low-resolution phases is presented which uses histogram matching not only of the electron density, but also of histograms obtained from the different levels of detail provided by the wavelet transform of the electron density. Statistical values for the wavelet coefficients can be predicted and depend only on the resolution and solvent content. Therefore, new details can be added to an electron-density map by matching the values of the wavelet coefficients to those predicted for an increased resolution. The positions of the new details are also guided by the diffraction pattern. In this way, the resolution can be increased gradually; on a number of trial structures of different size, solvent percentage and space group, it has been possible to extend the phasing from 10,Å to around 6,7,Å. [source] 4122: Exploring new strategies to record and analyse clinical electroretinogramsACTA OPHTHALMOLOGICA, Issue 2010P LACHAPELLE Purpose Investigate if the combination of time-frequency domain analysis and ERG dipole rotation reveals hidden features of the normal ERG that could be instrumental in the interpretation of nearly extinguished ERG responses. Methods Analyses were conducted on photopic ERGs (Photopic Hills: PH) obtained from normal subjects (n=75) and patients (n=65) affected with various retinopathies. A Discrete Wavelet Transform (DWT) was done on each ERGs and key descriptors (Holder exponent and wavelet coefficient maxima) were calculated. Dipole rotation was obtained by combining 11 gaze positions (0, 8, 16, 24, 32 and 40 degrees nasal or temporal to center) with 4 electrode locations [corneal (CE), lower lid (LL), external (EC) and internal canthi (IC)]. Results The Holder exponent follows a parabola, while some of the local wavelet maxima seem to follow a PH-like like distribution (b-wave and OPs) or a logistic growth function (a-wave). In still recordable pathological ERGs, the wavelet maxima matched that found in normal ERGs evoked at low stimulus intensities while in nearly extinguished ERGs (<10% of normal) the wavelet coefficients were significantly lower. Irrespective of the direction of gaze, there was little variation in DTL ERGs. EC ERGs were the only ones to reverse in polarity (seen 5 degrees nasal to fixation in nasal to temporal shift). Conclusion The parameters obtained with the DWT offers useful and reproducible tools to help identify subtle features of residual ERGs and therefore should allow for a more accurate quantification of low-voltage ERGs responses. Finally, our results suggest that varying the gaze and electrode positions would represent a valuable addition to the recording of clinical ERGs. Funded by NSERC. [source] | ||||||||||||||||||||||